Mechanically stimulated self-powered electrochemical sensors: principles, classifications, and future directions
- Authors
- Kang, Jiseung; Um, Dae-Yong; Lee, Sanghyun; Kim, Seonghyeon
- Issue Date
- Jan-2026
- Publisher
- Frontiers Media S.A.
- Keywords
- self-powered; electrochemical reaction; sensors; mechanism; redox reaction
- Citation
- Frontiers in Materials, v.12
- Indexed
- SCIE
SCOPUS
- Journal Title
- Frontiers in Materials
- Volume
- 12
- URI
- https://scholarworks.gnu.ac.kr/handle/sw.gnu/82183
- DOI
- 10.3389/fmats.2025.1727201
- ISSN
- 2296-8016
- Abstract
- The rapid advancement of self-powered sensor (SPS) technology has enabled continuous and autonomous monitoring across various domains, including biomedical, environmental, and structural applications. Conventional energy-harvesting mechanisms, such as triboelectric, piezoelectric, and electromagnetic induction, produce transient AC-type signals that are prone to drift, attenuation, and poor response under static or low-frequency conditions. Conversely, self-powered electrochemical sensors (SPESs), which operate via mechanically induced modulation of interfacial redox kinetics and ion transport generate stable, quasi-steady-state outputs via Faradaic charge transfer and electrochemical potential variations at the electrode-electrolyte interface. These devices exhibit high sensitivity to both dynamic and static stimuli, presenting operational longevity and material adaptability for long-term sensing applications. Recent advances in hierarchical electrode architectures, multifunctional ionic hydrogels, and hybrid redox systems have further enhanced the energy conversion efficiency, mechanical robustness, and multimodal responsiveness. In this mini-review, we summarize the working mechanisms, material strategies, and classification of mechanically driven SPSs based on the stimulus type. We discuss key challenges such as the limited output power, environmental cross-sensitivity, and reproducibility. Furthermore, we discuss future research directions focused on developing scalable, intelligent, and multimodal self-powered sensing platforms for next-generation IoT and diagnostic systems.
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